Social networking platforms provide information that may be potentially relevant for various organizations such as marketing organizations. Such information may be available publicly on the social networking platforms, which may enable business organizations to monitor and track user's activity on the social networking platforms and further identify potential customers for their respective services and products.
Existing tools and techniques utilize keyword based or query based searches called “stream definitions” to extract user conversations (plurality of messages) over the social networking platforms. However, such stream definitions or search queries need to be manually updated to identify changing behavior of customer preferences, occurring events and trending topics over a period of time. Further, such queries may lead to extraction of redundant and noisy conversations/spam messages that may make manual analysis of the extracted messages difficult for business organizations.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one skilled in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.